Eliminate Unplanned Downtime

Condition based maintenance: using big data to lower costs, improve efficiency, and reduce maintenance.

Big data is staying true to its name – it’s doing big things. While great data-based strides have been made in medicine, entertainment, and education, we’re still seeing its potential begin to bloom as an industrial tool that can help companies make their operations efficient, systematic, and predictable.

Despite the fact that we’re only pioneers at the frontier of the industrial internet of things, the future of maintenance is clear – we are headed toward a world where the use of enormous amounts of data will allow companies to learn, months or years in advance, which specific machinery assets are in need of service and when they’re likely to fail. And, perhaps most importantly, this is a world in which a company can eliminate the costs incurred to business when a machinery asset unexpectedly fails.

Because we’re only at the onset of these exciting developments, it makes sense to put these technologies into context through an example. Imagine you’re a food producer, perhaps you raise cows for milk. One day the machinery you use to milk your cows breaks down. You call your engineer but she tells you that the machine needs a specific part that can only be brought in tomorrow. Suddenly, you can’t produce the milk that you are contractually required to provide to the grocery chain in town. In all likelihood, that grocery chain and you have a strict contract in which it is stipulated that if you don’t provide milk at a certain time and of a certain quantity, you pay a fine. All of this could be avoided if only you knew that your milking machinery would break down. Had you known, you could have scheduled the maintenance, brought in that part months ago, and completed the servicing so that it wouldn’t interfere with your operation.

This example is a simple one. For most companies, a breakdown somewhere along their supply chain can mean thousands of euros of costs per hour and unhappy partners and clients up and down the chain. Smart Condition Monitoring based on sensors and artificial intelligence eliminates that risk.  Through the collection and analysis of mass amounts of your data, an online monitoring system continually determines the health of an asset and predicts its remaining useful lifetime. It is a building block of Condition Based Maintenance (CBM), a regime that allows for scheduled maintenance so that no surprise breakdowns occur and no unnecessary costs are incurred.

Condition Based Maintenance, as built off of such data analysis, allows an asset owner to maintain machinery in a way that’s only just become possible. When a company knows when its machinery will fail and in what way, it allows for a vast increase in efficiency overall. Eliminated is the risk that on any given day a motor might fail. While the use CBM doesn’t mean that your machinery will never fail, it does mean that you will know exactly when an asset will and gives the luxury of planning out, months in advance, when to service assets so that they never fail unexpectedly, improving your overall efficiency as a company.

It’s also much cheaper and more efficient to make small fixes to a machine in advance compared to fixing a big failure at the last minute. CBM enables asset owners to make regular maintenance of targeted problems based on real knowledge a reality. In other words, when you maintain your equipment, big expensive machinery failures are much less likely to occur. CBM allows asset owners to do just that. It predicts those small things that, if over time with diligent maintenance are serviced, hugely reduce the risk of major, expensive failures. CBM enables informed maintenance based on real data. This can mean a huge reduction in overall costs of machine upkeep and, of course, the elimination of failures.

While Smart Condition Monitoring and CBM are still a moving, growing area of technology, it is a strong, exciting path toward the future. As more companies collect bigger amounts of data, analyses will be even better at predicting how machinery will act, only improving efficiency and reducing costs more. Regardless of how this field grows, it’s undeniable that we are on the cusp of a fundamental change in industry, a development that will alter how companies use machinery and how machinery is maintained.